Your first site analysis

This walkthrough follows a realistic brief: a coffee operator wants to open one more café in Austin and needs a short list of locations, a visit forecast for the favorite and a demographic profile to take to the landlord. It uses four SDSS tools in sequence and ends with a saved project your team can reopen. Expect to use three or four of your monthly analyses.

All figures come from MapDove’s synthetic demo dataset (how it is generated), so treat the outputs as illustrative of the workflow, not as advice for a real lease.

Step 1 — Score the city

Open Site selection → Suitability, choose Austin and the category Coffee & Cafés.

The suitability tool is a weighted multi-criteria overlay: every hex gets a 0–100 score from nine criteria you weight from 0 to 10. A reasonable starting point for a café:

Criterion	Weight	Why
Daytime population	8	Coffee is a weekday, office-hour trade
Population density	5	Residents generate weekend visits
Traffic	6	Visibility and drive-by capture
Competition	6	Inverted — fewer cafés nearby scores higher
Complementary	4	Gyms, apparel, malls and pharmacies feed coffee
Income	3	Supports price tier
Rent	3	Inverted — cheaper areas score higher
Growth	2	Favor neighborhoods that are adding people
Demand gap	7	Demand that existing cafés do not reach well

Leave Top sites at 10 and Minimum spacing at 1.5 km, then click Run.

What you get back:

• A hex map shaded by score. Hover a hex to see each criterion’s normalized value (0–1).
• A ranked list of top sites — the best-scoring hexes, picked greedily so that no two are closer than the minimum spacing. This keeps the list from being ten neighboring hexes in the same block.
• Score stats (min, mean, max) to judge how differentiated the city is for these weights.

Try changing one weight at a time and re-running. If the top sites barely move, your result is robust; if they jump around, the decision depends heavily on that criterion and deserves a conversation. Details on each criterion: Site suitability.

Step 2 — Forecast visits at the favorite

Click the top-ranked site (say, a hex in Mueller) and choose Forecast visits, or open Site selection → Huff forecast and drop a pin.

Set the format of the planned store:

• Size (sq ft) — leave empty to use the category’s typical size (1,800 sq ft for coffee). Bigger stores are more attractive, with diminishing returns.
• Brand strength — 0.6 to 1.5, where 1.0 is an average operator. A strong, well-known chain might use 1.1–1.2.

Run it. The Huff model returns:

• Predicted daily and annual visits, calibrated against the observed visits of the existing cafés in the city.
• Market share of modeled category demand the new store would capture.
• The category average daily visits for comparison.
• Cannibalization — the existing cafés that lose the most visits, with their current daily visits, the expected change and the percent change. If your own brand operates some of them, this is the number that matters most.
• The candidate’s modeled trade area (the hexes that supply the first ~80% of its visitors).

See Huff visit forecast and Cannibalization for interpretation.

Step 3 — Profile the catchment

From the same pin choose Catchment (or open Site selection → Catchment). Pick up to four rings and a unit:

• mi or km — straight-line radius rings, for example 0.5, 1 and 3 miles.
• min — drive-time rings, for example 5, 10 and 15 minutes.

Drive-time rings in MapDove are a speed-based radius approximation: minutes are converted to a radius using the city’s average driving speed (42 km/h in Austin). They are circles, not road-network isochrones, so they do not reflect rivers, highways or one-way streets. Use them for quick comparisons, not for a final drive-time study. See Catchments.

Each ring reports population, households, daytime population, median income and age, education and family shares, traffic and rent indices, growth, the lifestyle-segment mix and the count of places by category inside it.

Step 4 — Compare the shortlist

Pick the three best candidates from Step 1 and open Site selection → Compare sites. Add up to five labeled sites, keep the same weights, and run. Each site gets its suitability score and criteria, a Huff forecast (without the trade-area polygons) and a one-mile (Dubai: 1.6 km) catchment, side by side. Usually this is where the discussion moves from “where is good” to “which trade-off do we prefer”. See Compare sites.

Step 5 — Save as a project

Click Save as project on any result. Give it a name such as Austin café #12 — shortlist, add notes (assumptions, who asked, next steps) and save. Projects keep:

• the kind of analysis (site_selection, huff, catchment, compare or trade_area),
• the city, the exact parameters you used and the result,
• notes, the author and timestamps.

Everyone in your organization can reopen a project from Projects, re-run it with the same parameters, or edit its notes. Re-running counts as a new analysis.

Checklist before you share results

• State that the inputs are demo data and the drive-time rings are approximations.
• Note the weights used — suitability scores are only meaningful with their weights.
• Include the cannibalization table if the operator already has stores nearby.
• Keep the project link so the analysis can be reproduced.

Next: learn the vocabulary in Core concepts, or read the formulas in Methodology.